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Dogs vs Cats Image Classification Without Image Augmentation

In this tutorial, we will discuss how to classify images into pictures of cats or pictures of dogs. We'll build an image classifier using tf.keras.Sequential model and load data using tf.keras.preprocessing.image.ImageDataGenerator.

Specific concepts that will be covered:

In the process, we will build practical experience and develop intuition around the following concepts

  • Building data input pipelines using the tf.keras.preprocessing.image.ImageDataGenerator class — How can we efficiently work with data on disk to interface with our model?
  • Overfitting - what is it, how to identify it?

Before you begin

Before running the code in this notebook, reset the runtime by going to Runtime -> Reset all runtimes in the menu above. If you have been working through several notebooks, this will help you avoid reaching Colab's memory limits.

Importing packages

Let's start by importing required packages:

  • os — to read files and directory structure
  • numpy — for some matrix math outside of TensorFlow
  • matplotlib.pyplot — to plot the graph and display images in our training and validation data

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import tensorflow as tf

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from tensorflow.keras.preprocessing.image import ImageDataGenerator

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import os
import matplotlib.pyplot as plt
import numpy as np

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import logging
logger = tf.get_logger()

Data Loading

To build our image classifier, we begin by downloading the dataset. The dataset we are using is a filtered version of Dogs vs. Cats dataset from Kaggle (ultimately, this dataset is provided by Microsoft Research).

In previous Colabs, we've used TensorFlow Datasets, which is a very easy and convenient way to use datasets. In this Colab however, we will make use of the class tf.keras.preprocessing.image.ImageDataGenerator which will read data from disk. We therefore need to directly download Dogs vs. Cats from a URL and unzip it to the Colab filesystem.

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_URL = ''
zip_dir = tf.keras.utils.get_file('', origin=_URL, extract=True)

The dataset we have downloaded has the following directory structure.

|__ train
    |______ cats: [cat.0.jpg, cat.1.jpg, cat.2.jpg ...]
    |______ dogs: [dog.0.jpg, dog.1.jpg, dog.2.jpg ...]
|__ validation
    |______ cats: [cat.2000.jpg, cat.2001.jpg, cat.2002.jpg ...]
    |______ dogs: [dog.2000.jpg, dog.2001.jpg, dog.2002.jpg ...]

We can list the directories with the following terminal command:

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zip_dir_base = os.path.dirname(zip_dir)
!find $zip_dir_base -type d -print

We'll now assign variables with the proper file path for the training and validation sets.

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base_dir = os.path.join(os.path.dirname(zip_dir), 'cats_and_dogs_filtered')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')

train_cats_dir = os.path.join(train_dir, 'cats')  # directory with our training cat pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')  # directory with our training dog pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')  # directory with our validation cat pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')  # directory with our validation dog pictures

Understanding our data

Let's look at how many cats and dogs images we have in our training and validation directory

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num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))

num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))

total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val

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print('total training cat images:', num_cats_tr)
print('total training dog images:', num_dogs_tr)

print('total validation cat images:', num_cats_val)
print('total validation dog images:', num_dogs_val)
print("Total training images:", total_train)
print("Total validation images:", total_val)

Setting Model Parameters

For convenience, we'll set up variables that will be used later while pre-processing our dataset and training our network.

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BATCH_SIZE = 100  # Number of training examples to process before updating our models variables
IMG_SHAPE  = 150  # Our training data consists of images with width of 150 pixels and height of 150 pixels

Data Preparation

Images must be formatted into appropriately pre-processed floating point tensors before being fed into the network. The steps involved in preparing these images are:

  1. Read images from the disk
  2. Decode contents of these images and convert it into proper grid format as per their RGB content
  3. Convert them into floating point tensors
  4. Rescale the tensors from values between 0 and 255 to values between 0 and 1, as neural networks prefer to deal with small input values.

Fortunately, all these tasks can be done using the class tf.keras.preprocessing.image.ImageDataGenerator.

We can set this up in a couple of lines of code.

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train_image_generator      = ImageDataGenerator(rescale=1./255)  # Generator for our training data
validation_image_generator = ImageDataGenerator(rescale=1./255)  # Generator for our validation data

After defining our generators for training and validation images, flow_from_directory method will load images from the disk, apply rescaling, and resize them using single line of code.

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train_data_gen = train_image_generator.flow_from_directory(batch_size=BATCH_SIZE,
                                                           target_size=(IMG_SHAPE,IMG_SHAPE), #(150,150)

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val_data_gen = validation_image_generator.flow_from_directory(batch_size=BATCH_SIZE,
                                                              target_size=(IMG_SHAPE,IMG_SHAPE), #(150,150)

Visualizing Training images

We can visualize our training images by getting a batch of images from the training generator, and then plotting a few of them using matplotlib.

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sample_training_images, _ = next(train_data_gen)

The next function returns a batch from the dataset. One batch is a tuple of (many images, many labels). For right now, we're discarding the labels because we just want to look at the images.

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# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.
def plotImages(images_arr):
    fig, axes = plt.subplots(1, 5, figsize=(20,20))
    axes = axes.flatten()
    for img, ax in zip(images_arr, axes):

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plotImages(sample_training_images[:5])  # Plot images 0-4

Model Creation

Define the model

The model consists of four convolution blocks with a max pool layer in each of them. Then we have a fully connected layer with 512 units, with a relu activation function. The model will output class probabilities for two classes — dogs and cats — using softmax.

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model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),

    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.Dense(512, activation='relu'),

Compile the model

As usual, we will use the adam optimizer. Since we output a softmax categorization, we'll use sparse_categorical_crossentropy as the loss function. We would also like to look at training and validation accuracy on each epoch as we train our network, so we are passing in the metrics argument.

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Model Summary

Let's look at all the layers of our network using summary method.

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Train the model

It's time we train our network.

Since our batches are coming from a generator (ImageDataGenerator), we'll use fit_generator instead of fit.

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EPOCHS = 100
history = model.fit_generator(
    steps_per_epoch=int(np.ceil(total_train / float(BATCH_SIZE))),
    validation_steps=int(np.ceil(total_val / float(BATCH_SIZE)))

Visualizing results of the training

We'll now visualize the results we get after training our network.

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acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(EPOCHS)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')

As we can see from the plots, training accuracy and validation accuracy are off by large margin and our model has achieved only around 70% accuracy on the validation set (depending on the number of epochs you trained for).

This is a clear indication of overfitting. Once the training and validation curves start to diverge, our model has started to memorize the training data and is unable to perform well on the validation data.